# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import torch
import torch.nn as nn
import torch.nn.functional as F
import time

def mask_from_lens(lens, max_len=None):
    if max_len is None:
        max_len = lens.max()
    ids = torch.arange(0, max_len, device=lens.device, dtype=lens.dtype)
    mask = torch.lt(ids, lens.unsqueeze(1))
    return mask
    
    
class AttentionCTCLoss(torch.nn.Module):
    def __init__(self, blank_logprob=-1):
        super(AttentionCTCLoss, self).__init__()
        self.log_softmax = torch.nn.LogSoftmax(dim=3)
        self.blank_logprob = blank_logprob
        self.CTCLoss = nn.CTCLoss(zero_infinity=True)

        self.in_lens = 200
        self.out_lens = 900

    def forward(self, attn_logprob, in_lens, out_lens):
        key_lens = in_lens
        query_lens = out_lens

        in_lens_mask = mask_from_lens(in_lens, 200).int()
        in_lens_mask = F.pad(input=in_lens_mask.cpu(),
                             pad=(1, 0, 0, 0),
                             value=1).to(attn_logprob.device)
        out_lens_mask = mask_from_lens(out_lens, 900).int()

        attn_logprob_padded = F.pad(input=attn_logprob.cpu(),
                                    pad=(1, 0, 0, 0, 0, 0, 0, 0),
                                    value=self.blank_logprob).to(attn_logprob.device)

        cost_total = 0.0
        for bid in range(attn_logprob.shape[0]):
            target_seq = torch.arange(1, self.in_lens + 1).unsqueeze(0).npu()
            curr_logprob = attn_logprob_padded[bid].permute(1, 0, 2)
            curr_logprob = self.log_softmax(curr_logprob[None])[0]

            mask = out_lens_mask[bid].unsqueeze(1) @ in_lens_mask[bid].unsqueeze(0)
            curr_logprob = curr_logprob * mask.unsqueeze(1)

            ctc_cost = self.CTCLoss(
                curr_logprob, target_seq, input_lengths=query_lens[bid:bid+1],
                target_lengths=key_lens[bid:bid+1])

            cost_total += ctc_cost
        cost = cost_total / attn_logprob.shape[0]
        return cost
        

class AttentionBinarizationLoss(torch.nn.Module):
    def __init__(self):
        super(AttentionBinarizationLoss, self).__init__()

    def forward(self, hard_attention, soft_attention, eps=1e-12):
        mask = (hard_attention == 1).float()
        log = torch.log(torch.clamp(soft_attention,
                            min=eps))
        log_sum = (log * mask).sum()

        return -log_sum / hard_attention.sum()